class TestPlotting(TestCase):
def setUp(self) -> None:
self.predictors = [
nc.band1, nc.band2, nc.band3, nc.band4, nc.band5, nc.band7
]
self.stack = Raster(self.predictors)
self.stack_single = Raster(self.predictors[0])
def tearDown(self) -> None:
self.stack.close()
self.stack_single.close()
def test_plotting_raster(self):
# test basic raster matrix plot
p = self.stack.plot()
self.assertIsInstance(p, np.ndarray)
# test with arguments
p = self.stack.plot(
cmap="plasma",
norm=mpl.colors.Normalize(vmin=10, vmax=100),
title_fontsize=10,
label_fontsize=10,
names=["band1", "band2", "band3", "band4", "band5", "band7"],
figsize=(10, 5),
legend_kwds={"orientation": "horizontal"})
self.assertIsInstance(p, np.ndarray)
def test_plotting_single(self):
p = self.stack_single.plot(legend_kwds={
"orientation": "horizontal",
"fraction": 0.04
})
self.assertIsInstance(p, mpl.axes.Subplot)
def test_plotting_single(self):
stack = Raster(self.predictors[0])
p = stack.plot(legend_kwds={
"orientation": "horizontal",
"fraction": 0.04
})
self.assertIsInstance(p, mpl.axes.Subplot)
def test_plotting_raster(self):
stack = Raster(self.predictors)
# test basic raster matrix plot
p = stack.plot()
self.assertIsInstance(p, np.ndarray)
# test with arguments
p = stack.plot(
cmap="plasma",
norm=mpl.colors.Normalize(vmin=10, vmax=100),
title_fontsize=10,
label_fontsize=10,
names=["band1", "band2", "band3", "band4", "band5", "band7"],
figsize=(10, 5),
legend_kwds={"orientation": "horizontal"})
self.assertIsInstance(p, np.ndarray)
import matplotlib.pyplot as plt import pyspatialml.datasets.nc_dataset as nc import tempfile import rasterio # First, import the extract and predict functions: predictors = [nc.band1, nc.band2, nc.band3, nc.band4, nc.band5, nc.band7] # Create a RasterStack instance stack = Raster(predictors) stack.count # Perform band math ndvi = (stack.iloc[3] - stack.iloc[2]) / (stack.iloc[3] + stack.iloc[2]) ndvi = Raster(ndvi) ndvi.plot() tmp = ndvi.aggregate(out_shape=(50, 50)) tmp.plot() # Perform OR operation (union two layers) (stack.iloc[5] | stack.iloc[0]).plot() plt.show() # Perform AND operation (intersect two layers) (stack.iloc[5] & stack.iloc[0]).plot() plt.show() # Perform XOR operation (symmetrical difference) (stack.iloc[5] ^ stack.iloc[0]).plot() plt.show()